For crowded scenes, the accuracy of object-based computer vision methods declines when the images are low-resolution and objects have severe occlusions. Taking counting methods for example, almost all the recent state-of-the-art counting methods bypass explicit detection and adopt regression-based methods to directly count the objects of interest. Among regression-based methods, density map estimation, where the number of objects inside a subregion is the integral of the density map over that subregion, is especially promising because it preserves spatial information, which makes it useful for both counting and localization (detection and tracking). With the power of deep convolutional neural networks (CNNs) the counting performance has improved steadily. The goal of this paper is to evaluate density maps generated by density estimation methods on a variety of crowd analysis tasks, including counting, detection, and tracking. Most existing CNN methods produce density maps with resolution that is smaller than the original images, due to the downsample strides in the convolution/pooling operations. To produce an original-resolution density map, we also evaluate a classical CNN that uses a sliding window regressor to predict the density for every pixel in the image. We also consider a fully convolutional adaptation, with skip connections from lower convolutional layers to compensate for loss in spatial information during upsampling. In our experiments, we found that the lower-resolution density maps sometimes have better counting performance. In contrast, the original-resolution density maps improved localization tasks, such as detection and tracking, compared with bilinear upsampling the lower-resolution density maps. Finally, we also propose several metrics for measuring the quality of a density map, and relate them to experiment results on counting and localization.

1. Beyond Counting: Comparisons of
Density Maps for Crowd Analysis Tasks -
Counting, Detection, and Tracking
Di Kang, Zheng Ma, and Antoni B. Chan
IEEE Transactions on Circuits and Systems for
Video Technology (TCSVT)
Presented by:
Tarik Reza Toha
#1017052013

2. • Problem definition
– What is the research problem?
• Motivation
– Why have the authors done the research?
• Solution approach
– How have the authors solved the problem?
– Be detail on this.
• Subsequent advancements
– What are the subsequent research studies and how have
they further advanced the solution of the problem?
2
Outline

3. 3
Crowd Analysis
• Automatic analysis of
crowded scenes
facilitates:
– Crowd management
– Traffic control
– Urban planning
– Surveillance
• Counting, detection, and
tracking are very
challenging
– Low resolution videos
– Heavy inter-occlusion

4. 4
Density Map
Density maps can be directly used
to localize individual object

5. 5
Estimating Density Maps
Density maps can be estimated by
using deep neural networks

6. • Regression-based counting
– Regression-based counting methods directly map
from the image features to the number of people,
without explicit object detection
– Gives better performance for crowded scenes by
bypassing the hard detection problem
– Ignores the distribution information of the objects
within the region
6
Existing Crowd Counting Methods
It cannot be used
for object
localization

7. • Density-based counting
– Density values are estimated from low-level features that
maintains location information
– The current CNNs produce reduced-resolution density maps
7
Existing Crowd Counting Methods (contd.)
Accurate detection
requires original
resolution maps

8. 8
Main Contribution

9. 9
Proposed Architecture
Predicts the density value at the
center of the image patch

10. 10
Proposed Architecture (contd.)
Predicts the number of people in the
image patch

11. 11
Proposed Architecture (contd.)
Density maps prevents
the tracker from drifting to the
background

12. 12
Quality of Density Maps
MCNN, CNN-pixel, and FCNN-
skip show the best correlation
between ground-truth and
prediction

13. 13
Quality of Density Maps (contd.)
CNN-pixel has the best localization
and similar compactness, while
FCNN-skip has good localization
but less compactness

14. 14
Quality of Density Maps (contd.)
Both CNN-pixel and FCNN-skip
perform good on temporal
smoothness

15. 15
Experimental Evaluation

16. 16
WorldExpo’10 Dataset

17. 17
WorldExpo’10 Dataset (contd.)
Both CNN-pixel and FCNN-skip
perform similarly

18. 18
UCF_CC_50 Dataset

19. 19
TRANCOS Dataset

20. 20
TRANCOS Dataset (contd.)
FCNN-skip has the
lowest MAE, GAME(1), and
GAME(2) among the methods

21. 21
Detection Experiments

22. 22
Tracking Experiments
Using CNN-pixel yields
the largest increase in precision
compared to other methods

23. • FCNN produces reduced-resolution density maps
that performs well at counting
– Their accuracy diminished at localization tasks due to
the loss of spatial resolution
• CNN-pixel produces the highest quality density
map for localization tasks, while degrading
counting task performance
– It suffers from higher computational complexity
compared to FCNN
23
Conclusion

24. Thank you
Questions are welcome!
24